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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics Driving Engagement in Daily Fantasy Sports with a Scalable and Urgency-Aware Ranking Engine TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds Hybrid Retrieval for COVID-19 Literature: Comparing Rank Fusion and Projection Fusion with Diversity Reranking FRAGATA: Semantic Retrieval of HPC Support Tickets via Hybrid RAG over 20 Years of Request Tracker History Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines Indexing Multimodal Language Models for Large-scale Image Retrieval FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents Knowledge Graph RAG: Agentic Crawling and Graph Construction in Enterprise Documents NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds ATANT v1.1: Positioning Continuity Evaluation Against Memory, Long-Context, and Agentic-Memory Benchmarks Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation HeceTokenizer: A Syllable-Based Tokenization Approach for Turkish Retrieval NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong Regime-Conditional Retrieval: Theory and a Transferable Router for Two-Hop QA MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits PRAGMA: Revolut Foundation Model Rag Performance Prediction for Question Answering Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model SocialWise: LLM-Agentic Conversation Therapy for Individuals with Autism Spectrum Disorder to Enhance Communication Skills Working Notes on Late Interaction Dynamics: Analyzing Targeted Behaviors of Late Interaction Models Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval Spectral Tempering for Embedding Compression in Dense Passage Retrieval AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics Exploring Structural Complexity in Normative RAG with Graph-based approaches: A case study on the ETSI Standards SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults RLPO: Residual Listwise Preference Optimization for Long-Context Review Ranking When & How to Write for Personalized Demand-aware Query Rewriting in Video Search Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows WisPaper: Your AI Scholar Search Engine GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs Hierarchical Semantic Retrieval with Cobweb WARBERT: A Hierarchical BERT-based Model for Web API Recommendation Reliable Evaluation Protocol for Low-Precision Retrieval VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
The Number of Terms and Documents for Pseudo-Relevant Feedback for Ad-hoc Information Retrieval
Abderrahim Mohammed El Amine, Benameur Said, Abderrahim Mohammed · 2013-06-18 · via cs.IR updates on arXiv.org

In Information Retrieval System (IRS), the Automatic Relevance Feedback (ARF) is a query reformulation technique that modifies the initial one without the user intervention. It is applied mainly through the addition of terms coming from the external resources such as the ontologies and or the results of the current research. In this context we are mainly interested in the local analysis technique for the ARF in ad-hoc IRS on Arabic documents. In this article, we have examined the impact of the variation of the two parameters implied in this technique, that is to say, the number of the documents «D» and the number of terms «T», on an Arabic IRS performance. The experimentation, carried out on an Arabic corpus text, enables us to deduce that there are queries which are not easily improvable with the query reformulation. In addition, the success of the ARF is due mainly to the selection of a sufficient number of documents D and to the extraction of a very reduced set of relevant terms T for retrieval.

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